Does checking and correcting your AI more often always help, or can it backfire when it only has a few tasks to learn from?

This explores whether running feedback cycles more often can backfire when the tasks being learned from are a small, fixed set. The corpus has no note that tests loop frequency against task-set size directly, so what follows is inferred from neighboring findings. They suggest that more loops usually help, and that when loops hurt, the cause is what the loop feeds on and what it carries, not how often it runs.

Frequency on its own looks like a virtue. Across 36 long optimization tasks, repeated benchmark-edit-incorporate cycles predicted success better than how good the first attempt was. Most models failed by quitting early or wasting their budget, not by looping too much What predicts success in ultra-long-horizon agent tasks?. Frequent feedback isn't the danger. The danger is a loop that keeps feeding a narrow pool of examples back into itself.

Self-feeding loops do damage. Ranking systems trained on data selected by their own past decisions settle into degenerate equilibria that amplify those decisions, unless selection bias is modeled explicitly Why do ranking systems need to model selection bias explicitly?. A finite task set is the same problem in miniature, because each pass shows the model more of what it already favors. Reinforcement learning shows the narrowing directly. It amplifies one dominant format from pretraining within the first epoch and suppresses the alternatives Does RL training collapse format diversity in pretrained models?. Task mix matters too. Structured domains push output entropy down while creative ones push it up, and training structured tasks first avoided entropy collapse that would otherwise damage open-ended ability Does training order reshape how models handle different task types?. Heavy repetition on one kind of task, then, can cost you abilities the loop never measures.

The signal inside the loop matters as much as the loop. Binary correctness rewards never penalize confident wrong answers, so repeated rounds reinforce confident guessing and degrade calibration Does binary reward training hurt model calibration?. Numerical rewards also plateau. Models stuck there produced correct solutions once given written critiques, because a number doesn't say why an attempt failed Can natural language feedback overcome numerical reward plateaus?. Feedback carries both an evaluative part (how well it went) and a directive part (how to change), and scalar rewards keep only the first Can scalar rewards capture all the information in agent feedback?. Once a thin signal has extracted what it can from a small task set, further loops repeat information you already have. Holistic reward models are also prone to overfitting superficial artifacts, and splitting them into checklist criteria reduces that Can breaking down instructions into checklists improve AI reward signals?.

The corpus points to a hypothesis, not a measured result. Frequent feedback is most likely to hurt when the loop is narrow (few tasks), thin (scalar rewards), and self-referential (learning from its own selections). The remedies these notes point to are richer signals, deliberate task ordering, and explicit correction for selection bias, not fewer loops.